DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks

1. DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks Shan-Hung Wu Kun-Ta Chuang Chung-Min Chen Ming-Syan Chen

2. Outline • Introduction • Related Work • Design of DIKNN • KNN Boundary Estimation • Performance Evaluation • Conclusion

3. Introduction • The problem of efficient KNN search has been a major research topic • traditional KNN query processing techniques assume location data are available in a centralized database and focus on improving the index performance. • “in-network” KNN query processing techniques for sensor networks rely on certain in-network infrastructure (index or data structure)distributed among the sensor nodes. • Drawbacks of the in-network approaches in large-scale mobile sensor network: distributedindexingstructrues, supernodes, fixed network

4. Introduction • A Density-aware Itinerary KNN query processing (DIKNN) for mobile sensor networks • key idea: let sensor nodes collect partial results and propagate the query along a well-devised ,conceptual itinerary structure • the first KNN processing technique dose not rely on any in-network indexing structure support: no constant maintenance or fixed data aggregation point • Several challenging issues arising in the design of DIKNN: estimate of search radius and design of efficient itinerary

5. Related Work • The centralized approach performs the queries in a centralized database containing locations of all the sensor nodes. • The in-network approach propagates the query directly among the sensor nodes in the network and collects relevant data to form the final result.

6. Related Work • The Peer-tree and DSI decentralize the index structures (R-tree) to distributed environments. A network is partitioned into a hierarchy of Minimum Bounding Rectangles (MBRs). • For KNN, index nodes become system bottlenecks easily and there are many unnecessary hops .

7. Related Work • KPT is proposed to handle the KNN query without fixed indexing. The KNN Perimeter Tree (KPT) builds upon GPSR for processing KNN queries. Two serious drawbacks: considerable overhead and conservative boundaery

8. Design of DIKNN • Definitions and Network Model • Definition 1 (k nearest neighbor problem) Given a set of sensor nodes S, a geographical location q (i.e., query point), and valid time T, find a subset S’ of S with k nodes (i.e., S’⊆ S, |S’| = k) such that at time T,∀n1 ∈ S’,n2 ∈S−S’:DIST(n1, q)≤DIST(n2, q), where DIST denotes the Euclidean distance function. • Network is under ad-hoc mode; all sensor nodes can store data locally and answer the queries individually; moving speed and directions are arbitrary; each sensor node is aware of its geo-location and maintains a table of IDs and locations of neighbor nodes falling within its radio range.

9. Design of DIKNN • Execution Phases • Routing phase: Q is geographically routed from s to the nearest neighbor around q • KNN boundary estimation phase:the home node estimates KNN boundary with radius R using KNNB algorithm • Query dissemination phase: the home node disseminates the query message to all sensor nodes inside the KNN boundary

10. KNN Boundary Estimation • Routing Phase • Q is routed from s to the nearest neighbor np around q utilizing the geographic face routing protocol (e.g.GPSR). An additional list L about information of the sensor network is sent along with Q. • On the ith hop to the destination ,the corresponding node appends its own location loci and the number of newly encountered neighbors enci to L.

11. KNN Boundary Estimation • Linear KNNB Algorithm • On receiving the query and list L,Nq estimats the KNN boundary by determining its radius length R. Determination of R must balance two conflicting factors. • A weaker assumption adopted by KNNB: sensor node are uniformly distributed only within the optimal KNN boundary (the boundary containing exactly k nearest neighbors).

12. KNN Boundary Estimation : the radius of the optimal KNN boundary ni : the corresponding node of the ith hop in the routing path D :density of nodes (nodes/m2) within the optimal KNN boundary

13. KNN Boundary Estimation

14. Design of DIKNN • Itinerary-Based Query Dissemination • On the KNN boundary is determined, the home node enters the query dissemination phase. Q-nodes are chosen for query dissemination D-nodes: neighbor nodes that are qualified to reply the query

15. Design of DIKNN • Primitives of itinerary-based solution • The itinerary width w specifies how close between segments of an itinerary. w= r/2 • Data collection from multiple D-nodes needs to be better scheduled to avoid collisions and delays. timer= • Discussions on the other issues (i.e., itinerary void)

16. Design of DIKNN • Concurrent itinerary structures KNN boundary is partitioned into multiple sectors. In each sector ,the query is propagated along a sub-itinerary. The distance between sub-itineraries in adjacent sectors is w to ensure full coverage of the KNN boundary when w ≤ r/2.

17. Design of DIKNN • Each sub-itinerary consists of three segments: the init-,adj- and peri-segments.

18. KNN Boundary Estimation • Interaction with Environments • Spatial Irregularity :when k is large, the sensor nodes tend to irregularity and their density becomes unpredictable. Inverse the direction of peri-segments in every interseptal sector. In such a configuration, the face-to-face adj-segments of different sub-itineraries together form rendezvous.

19. KNN Boundary Estimation • Interaction with Environments • Mobile Concern: Mobility of sensor nodes degrades query accuracy because nodes may move in or move out the KNN boundary during dissemination. • DIKNN address this issue in the query dissemination phase, where the last Q-node is obligated to determine how much farther a sub-itinerary should continue. g :assurance gain,0≤g≤1 μ:the fastest moving speed R’= R+g(te − ts)μ, ts and te denote timestamps for the start and end of the query dissemination.

20. Performance Evaluation • Settings and Performance Metrics • Query Latency: The elapsed time (in second) between the time a query is issued by the sink and the time the query responses are returned. • Energy Consumption: Amount of energy (in Joule) consumed in a simulation run. • Query Accuracy: the pre-accuracy and post-accuracy are measured separately in experiments.

21. Performance Evaluation • Scalability

22. Performance Evaluation • Impact of Network Dynamics

23. Conclusion • DIKNN integrates query propagation with data collection along a well-designed itinerary traversal. • A simple and effective KNNB algorithm has been proposed to estimate the KNN boundary under the trade-off between query accuracy and energy efficiency. • Dynamic adjustment of the KNN boundary has also been addressed to cope with spatial irregularity and mobility of sensor nodes.

24. Finish • Thank you!